[1]} and [2]} proposed the idea of multi-instance multi-relations to solve the problem of overlapping relations. They used probabilistic graphical models that take a bag of sentences containing two entities as input and find all possible relations between them. Similarly, [3]} used a feature-based model to jointly predict the relation between two entities and their fine-grained types. They used features like the head tokens of two entities, tokens of two entities, tokens between the two entities, their POS tags, ordering of the two entities, the distance between them, and the Brown clusterhttps://github.com/percyliang/brown-cluster of each token in their model. They proposed a joint optimization framework to learn the entity embeddings, relation embeddings, and fine-grained type embeddings of the entities together.
